Prostate cancer presentation and management in the Middle East
Bibliographic record
Abstract
BACKGROUND: Although prostate cancer is a prevalent malignancy worldwide, its clinical presentation and management in the Middle East are not well-documented. This study aims to provide insights into the initial clinical presentation and management of prostate cancer in this region. METHODS: A retrospective review was conducted on seven institutional databases from six Middle Eastern countries, including Türkiye, Lebanon, Iraq, Syria, Bahrain, and Jordan, to identify patients diagnosed with prostate cancer in 2021. Descriptive analysis was performed on the collected data to provide an overview of the demographic, clinical, and treatment variables. RESULTS: A total of 1,136 patients were identified with a median age of 70 (range, 50-84). Most patients (78%) received their prostate cancer diagnosis after presenting with symptoms, as opposed to routine PSA screening. At the time of diagnosis, 35% of men had clinical T3 or T4 disease, 54% with Stage IV disease and 50% with Gleason score ≥ 8. Regarding treatment, 20% of non-metastatic and 22% of metastatic patients received no treatment. CONCLUSION: Most men in this study sought prostate cancer evaluation due to symptoms and were subsequently diagnosed with advanced-stage disease, providing a foundation for future research aimed at understanding the underlying factors behind the observed trends and enabling informed interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".